Energy management of hybrid electric vehicles: A review of energy optimization of fuel cell hybrid power system based on genetic algorithm

Energy Conversion and Management 205 (2020) 112474

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Energy Conversion and Management journal homepage: www.elsevier.com/locate/enconman

Energy management of hybrid electric vehicles: A review of energy optimization of fuel cell hybrid power system based on genetic algorithm

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Xueqin Lü , Yinbo Wu, Jie Lian, Yangyang Zhang, Chao Chen, Peisong Wang, Lingzheng Meng School of the Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China

A R T I C LE I N FO

A B S T R A C T

Keywords: Fuel cell hybrid electric vehicle Energy management strategy Hybrid power system Genetic algorithm Optimization parameters and objectives

Under the background of current environmental pollution and serious shortage of fossil energy, the development of electric vehicles driven by clean new energy is the key to solve this problem, especially the hybrid electric vehicle driven by fuel cell is the most effective solution. Many scholars have found that the output performance of hybrid system is an important reason to determine the life of fuel cell. Unreasonable output will affect the control characteristics of the drive system, resulting in a series of serious consequences such as the reduction of the life of fuel cell hybrid power system. Therefore, the energy management strategy and performance optimization of hybrid system is the key to ensure the normal operation of the system. At present, many excellent researchers have carried out relevant research in this field. Genetic algorithm is a heuristic algorithm, which has better optimization performance. It can easily choose satisfactory solutions according to the optimization objectives, and make up for these shortcomings by using its own characteristics. These characteristics make genetic algorithm have outstanding advantages in the iterative optimization of energy management strategy. This paper analyzes and summarizes the optimization effect of genetic algorithm in various energy management strategies, aiming to analyze and select the optimization rules and parameters, optimization objects and optimization objectives. This paper hopes to provide guidance for the optimal control strategy and structural design of the fuel cell hybrid power system, contribute to the research on improving the energy utilization efficiency of the hybrid power system and extending the life of the fuel cell, and provide more ideas for the optimization of energy management in the future.

1. Introduction With the development of science and technology, automobile has completed the transformation from invention to mass production and become an indispensable means of transportation for every family [1,2]. Due to the high reliability and mobility required by drivers, most of the automobiles used internal combustion engines (ICEs) in the past, and the main fuel was petroleum [3,4]. With the consideration of saving energy and reducing consumption and solving the problem of environmental pollution, the research and development and popularization of various electric vehicles (EVs) are increasing all over the world. Especially with the maturity of fuel cell technology, the application of fuel cell in automobile has attracted extensive attention of industry and academia [5,6]. Fuel cell hybrid electric vehicle (FCHEV) is one of the most potential transportation vehicles in the future [7]. The system has the advantages of high energy consumption and low waste rate [8,9]. This is because the fuel is directly converted into electrical energy [10,11]. In hybrid electric vehicles (HEVs), the key to

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reduce the cost is to improve the efficiency of energy utilization and improve the dynamic performance of the system [12]. Therefore, the optimal control strategy in energy management plays an important role in power system performance and cost reduction. 1.1. The development of electric vehicle worldwide Pure electric vehicles, HEVs and proton exchange membrane fuel cell (PEMFC) vehicles have developed rapidly [13]. The reports on electric vehicle sales in China Industrial Economy Information Network show the sales of electric vehicles over the past 10 years, as shown in Figs. 1 and 2. It can be seen that the global sales of EVs are almost exponential growth. Especially in the past few years, sales in China have exceeded 50% of total sales. The growing popularity of EVs also makes their research field more attractive. A large number of researchers are committed to the research and development of EVs and have made many breakthroughs in key technologies [5,14–16]. Fig. 2 shows the sales growth of ICE and electric vehicles over the last decade,

Corresponding author. E-mail address: [email protected] (X. Lü).

https://doi.org/10.1016/j.enconman.2020.112474 Received 15 November 2019; Received in revised form 2 January 2020; Accepted 3 January 2020 0196-8904/ © 2020 Elsevier Ltd. All rights reserved.

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Nomenclature BEV EMS EV FC FCHEV GA HEV HPS ICE LCI NN NSGA-II PEMFC PHEV SC

SOC xn J f (x ) ω g (x ) P C η ΔI j 2 m H2 Δt equadr ΔSOC req dem tar

battery electric vehicle energy management system electric vehicle fuel cell fuel cell hybrid electric vehicle genetic algorithm hybrid electric vehicle hybrid power system internal combustion engine life cycle impact neural network non-dominated Sorting Genetic Algorithm II proton exchange membrane fuel cell plug-in hybrid electric vehicle supercapacitor

state of charge solution vector fitness fitness function weight constraint function power cost efficiency current fluctuation hydrogen consumption sampling time average quadratic error differential value of SOC reference value demand value minimum target value

Fig. 1. The growing sales of EVs since 2009.

Fig. 2. The sale growth rate in China and around the world.

subsidies for new energy electric vehicles by the Chinese government, the rapid development and popularization of new energy electric vehicles in China. Based on current development trends, we can roughly predict the global development of electric vehicles in the next 20 years,

with the latter growing much faster than the former, and the government encouraging companies and users to turn their attention to the production and consumption of electric vehicles. Especially in 2014 and 2015, with the implementation of economic subsidies and policy 2

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and development of PEMFC technology is considered as one of the clean and efficient power generation technologies in the 21st century, and the technology is perfect for EV [26].

as shown in Fig. 3. 1.2. The topological structure and characteristics of various electric vehicles

The classification, advantages, disadvantages and typical applications of EVs are summarized as shown in Table 1. Among these new EVs, HEVs based on PEMFC have the characteristics of no charge and long driving distance, which are the most promising EVs [27–29]. PEMFC has the advantages of clean and environmental protection, fast start-up and short refueling time [30]. However, the output characteristic of PEMFC is relatively soft and its dynamic response ability is poor, so it is unable to recover the electric energy of motor braking feedback [31]. Therefore, batteries or large capacitors are needed as the energy storage system as the compensation energy [32–34]. Batteries and capacitors are used to better control the energy flow and are often helpful in improving vehicle efficiency compared to most conventional power systems [35]. However, in such HEVs, the current efficiency improvement is limited. The energy efficiency and service life of FC is one of the key problems restricting its large-scale commercialization [36,37]. Therefore, it is necessary to pay special attention to the optimization strategy research in energy management.

In order to reduce energy consumption and carbon dioxide emissions, in recent years, countries around the world are developing electric vehicles based on a variety of new energy. For example, hydrogen, natural gas, biogas and biofuels based on ethanol, biodiesel and methanol. There are many types of EVs corresponding to these energy sources. Among them, battery electric vehicle (BEV), hybrid electric vehicle (HEV), plug-in hybrid electric vehicle (PHEV) and fuel cell hybrid electric vehicle (FCHEV) are the four types of typical EVs that occupy the leading position. Here are four EVs: 1) BEV is a battery powered vehicle without ICE, most of which are directly driven by motor. The battery storage capacity of BEV is generally above 100kWh, and it needs external power supply for charging. Therefore, BEV is a plug-in electric vehicle [17]. Because of the large storage capacity of batteries, the price of BEV is higher, and there are little safety problems. 2) HEV refers to oil-electric hybrid vehicles, which use traditional ICE and motors as power sources, and some engines are modified to use other alternative fuels, such as ethanol fuels. Its battery capacity is very small, only when starting/stopping, acceleration/deceleration to supply/recover energy, and it can’t be charged externally and driven in pure electric mode, so it is basically run by ICE [18]. 3) PHEV is the combination of traditional ICE vehicle and BEV. Compared with the general HEV, PHEV has a larger energy storage system. The vehicle can be powered by using the stored energy charged from the grid. Compared with the traditional ICE vehicle and BEV, its ICE and battery are smaller [19]. In the PHEV, there are usually two types of energy and actuators, the engine and the battery pack. In order to improve fuel economy and extend battery life, the energy distribution between the two kinds of energy is necessary. PHEV can be charged from the grid, so they can operate in pure electric mode [20]. 4) The fuel cell (FC) electric vehicle is basically the same as the common EV in the power transmission system and control system, the main difference is the working principle of the power cell [21]. It is powered by FC, mostly PEMFC. The reaction mechanism of PEMFC is that chemical energy in fuel is directly converted into electrical energy without combustion, but through electrochemical reaction [22,23]. In fact, it is the reverse process of water electrolysis, which generates water and releases electricity through the chemical reaction of hydrogen and oxygen. In this process, only water and heat will be discharged [24,25]. Therefore, the research

1.3. Optimization control strategy of energy management Although PEMFC is very suitable for providing energy in theory, there are many obvious defects in practical application, which is far from the road of commercialization [38,39]. Although PEMFC is the fuel cell with the largest energy density at present, its maximum efficiency is less than 60% [40,41]. Under the condition of low dynamic response, another power supply is needed to meet the peak power requirements when the vehicle starts or accelerates, so as to achieve high dynamic response [42,43]. Therefore, relevant researchers and automobile manufacturers have put forward a hybrid vehicle manufacturing scheme with FC, battery or supercapacitor (SC) as the power, and make up for its shortcomings by using the performance differences of various components [44,45]. The power distribution between FCHEV and battery/SC module is a basic problem [46]. Lithium battery has the characteristics of high energy ratio, long service life and high output power [47,48]. SC is an electrochemical device with large discharge capacity and high energy conversion efficiency, which can compensate the peak power required for system operation [49,50]. However, due to the relatively low energy density, power can only be released in a short time [51]. FC and these two auxiliary power supplies can play their respective advantages [52]. When the vehicle decelerates and goes downhill, a lot of regenerative braking energy will be generated, which can be recycled between FC, battery and SC, so a reliable energy

Fig. 3. The global expected growth rate in the next 20 years. 3

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Table 1 Summary and analysis of the characteristics about 4 types of EV. Types

Main power system

Advantages

BEV

Battery

Relatively mature technology; Easy charging; Pollution-free

HEV

Gasoline + battery

PHEV

Gasoline + battery

FCHEV

FC + SC/battery

Long endurance; Convenient energy supply; Long battery life; Recoverable energy Easy charging; Little pollution; Good driving experience Pollution-free; Higher energy efficiency; Run smoothly and no noise.

Disadvantages

Typical applications

Long charging time; Short battery life; High cost and low economic benefit; Short service time A small amount of pollution; Long-distance driving does not save fuel

LEXUS NX; Toyota Prius

Complex structure and configuration; Higher cost than BEV

Audi A3 E-Tron; BWM 530Le

Immature technology; High cost and low security

Benz F-Cell; Toyota FCHV

Tesla Model-S; BMW i3; Benz EQC

The main factors affecting HPS are system performance, economic indicators and protection measures [66]. The control performance mainly includes dynamic response and steady-state characteristics [67]. The economy is mainly reflected in the minimization of hydrogen consumption, equivalent hydrogen consumption and life cycle cost [68]. The protection measures mainly include reducing current fluctuation and charging and discharging depth [69,70]. The optimization objective cannot be limited to the energy consumption of the system. Therefore, it is very important to choose the optimal HPS method and make the control strategy.

management strategy (EMS) is required [53]. At present, the commonly used methods for energy management of hybrid power system (HPS) include fuzzy rule control, deterministic rule control and global optimization method [54]. 1) Fuzzy rule control [55]: The management system based on fuzzy rules is a rule-based method composed of fuzzy logic [56]. Fuzzy logic refers to the method of judgment and reasoning by imitating the concept of uncertainty in human brain. Fuzzy logic can be adjusted at any time according to the degree of freedom of the system, so fuzzy control can be applied to the design of mechanical and electrical system of HPS. The limitation is that fuzzy rules mainly come from expert knowledge or control experience. Researchers can combine intelligent methods with fuzzy logic to give full play to its characteristics [57]. For example, Zhang et al. established an on-line fuzzy EMS to control the DCDC converter to achieve power balance. The experimental results show that the online fuzzy strategy can make full use of the energy of the battery and consume less hydrogen fuel [58]. 2) Deterministic rule control [59]: The control strategy based on deterministic rules can easily realize the real-time monitoring of hybrid power transmission system [60]. The instantaneous input is the only factor to consider in the decision-making of the controller. Performance is determined by a rule table or flow chart to meet driver and battery requirements in the most effective way. The strategy based on deterministic rules has the highest practicability. The limitation of deterministic rule control is similar to fuzzy rule, which is designed on the basis of expert knowledge. For example, Wang et al developed a rule-based distributed EMS for HPS. The proposed power allocation strategy considers the demand power, residual capacity and power capacity. The residual capacity and power of battery and supercapacitor are estimated by Bayes Monte Carlo method. The results show that the proposed strategy can extend the service life of the hybrid energy storage system and improve the economy of the system by using the charging and discharging limits of electric energy capacity and residual capacity [61]. 3) Global optimization [62]: The global optimization strategy solves the global solution according to the real-time change of the optimization problem [63]. Unlike classical optimization methods, which need some rules to solve problems, global optimization can be used to optimize irregular problems. For example, the traditional dynamic programming algorithm can effectively and stably solve the EMS based on global optimization, but the solution time is too long [64]. In another example, Liu et al. put forward a multi-objective layered prediction energy management strategy, which can achieve the optimal life-span economy and energy consumption economy of fuel cells within the prediction range [65].

1.4. The role of GA in optimization control The classical optimization methods are based on traditional calculus, and inevitably have some limitations, mainly as follows: 1) Most of the traditional optimization methods can only calculate the local optimal advantage of the objective function. For multi-peak function, these methods are often trapped in local optimal solution because of excessive pursuit of ‘descent’. 2) Many methods have high requirements for the smoothness and concavity of the objective function, and they can't do anything for the discrete function and the random function. Since the 1960s and 1970s, people have introduced artificial intelligence technology and biological evolution mechanism into optimization methods, gradually forming a group of completely different from traditional optimization methods, refreshing modern optimization methods [67]. Such as neural network, particle swarm optimization, dynamic programming, equivalent minimization, and so on. Many scholars have applied modern optimization methods to the energy management of HEVs and achieved fruitful results. Koo et al. built models using neural networks that provided real-time predictions without violating time constraints and saved energy consumption by avoiding unacceptable transitions [71]. In order to reduce the pressure caused by a large number of HEVs charging at the same time on the power system, Jahangir et al. predicted driving behavior by using the feedforward recursive artificial neural network based on rough structure training method [2]. Li et al. proposed a fuel cell/battery HEV adaptive EMS based on the minimum principle of Pontryagin [72]. They adopted an improved Markov based velocity prediction and particle swarm optimization method to identify the driving mode online. The results show that this strategy can predict the driving behavior of vehicles well and update the status of vehicles reasonably. Jiang et al. designed a two-dimensional dynamic programming algorithm to reduce energy consumption and system degradation [62]. A real - time energy management strategy based on two - dimensional Pontryagin minimum principle is proposed, and the results show that it has good energy saving effect and durability. Liu et al. proposed an adaptive equivalent 4

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energy consumption minimization strategy and an optimal power management strategy based on dynamic control distribution for HEVs, which significantly improved fuel economy [73]. In addition, Tan et al. proposed a new energy management strategy based on deep reinforcement learning framework to achieve optimal energy allocation [74]. These algorithms optimize EMS for different purposes and make great progress. However, genetic algorithm (GA) in evolutionary computing has become one of the most concerned algorithms among modern optimization algorithms because of its good global search performance and low algorithm complexity. GA is a random search method based on the law of biological evolution. In 1975, Professor Holland of the University of Michigan first proposed this view. It has inherent implicit parallelism and can directly act on structural objects without the limitation of function continuity. Using roulette and other methods, GA can automatically update the offspring of optimization results and global search direction. Because of these advantages, GA has been widely used in discrete optimization, algorithm learning, data processing and other fields. In addition, GA is also applicable to life prediction of big data, which plays an important role in life prediction and prolonging of fuel cells [75–79]. It is one of the important technologies in Artificial Intelligence Computing. Taking genetic algorithm as an example, this paper summarizes the optimization effect and parameters of the optimization algorithm for energy management. GA-based results mainly focus on the following research:

component life of electric vehicles, so EMS is very important. Although there are many methods of EMS for FCHEV, most of them are limited to initial power allocation, while GA plays a prominent role in the optimization strategy of HPS. Scholars have done a lot of research on GA, so that it can solve the problem of multi-objective optimization, it is easier to achieve the balance between reducing fuel consumption and other factors. These advantages are well suited to optimizing rules, parameters, or evaluation criteria previously proposed in EMS for better performance. This article will carefully review the process of these studies and summarize the optimization results. Different from previous reviews on EMS, this paper focuses on the optimization effect of EMS based on GA rather than the original EMS scheme. It mainly focuses on five aspects:

1) Structural optimization. In order to reach a good compromise between the capital investment and system reliability of the HPS, all components need to be optimally configured. Rullo et al. used GA to study the structural optimization of the economic prediction model of HPS [80]. To reduce the vehicle weight, Bhattacharjee et al. adopted multi-objective GA to optimize the transmission structure of the HPS [81]. The geometric structure of PEMFC bipolar channel is optimized by GA. The advantage of this method is to avoid all steps of design, including boundary setting, mesh generation and numerical calculation of design parameter changes. 2) Cost optimization. Erickson et al. studied the energy management optimization strategy of HPS [82]. One of the optimization strategies is to use reverse time window to optimize GA in real time, analyze the energy cost and maximum current rate of battery, and the cycle cost of EV using HPS. Compared with pure electric vehicle, this strategy can reduce the current rate of NEDC by 40% and get the best effect in low speed cycle. Habibollahzade et al. optimized SOFC and so on [83]. A multi-objective optimization method based on GA was used to optimize the gasification system with pure CO2 as gasifier. The results show that the optimization based on GA can control the operation under the optimum conditions, improve exergy efficiency and reduce total cost of products. 3) System performance optimization. Liu et al. studied online energy management based on driving status recognition [84]. The principal component analysis algorithm is used to classify the real historical driving data and construct four driving scenes. GA was used to search the optimal value of seven control actions offline. This method is close to the global optimal dynamic programming method, and has the characteristics of high real-time. Hu et al. used GA to optimize the discrete speed ratio, which not only reduced the energy loss and improved the energy economy of the system, but also improved the ride smoothness and driving efficiency [85].

2. Fuel cell and HPS structure and energy management optimization control

1) 2) 3) 4) 5)

the contents of GA based optimization control strategy; objectives of optimization strategy; selection of optimization parameters; optimization effect; unresolved issues.

Therefore, the remaining structure of this article is as follows. Section 2 briefly introduces FC, HPS and energy management optimization control. Section 3 describes the single-objective and multi-objective optimization process of GA. Section 4 summarizes the corresponding research results. Section 5 is the conclusion of this paper and the prospect of the future.

Even though PEMFC has the advantages of no pollution, no noise, simple operation and high battery efficiency, in practice, the application of PEMFC is still rare, far from being able to compete with ICE in the field of automobile energy supply [86]. In fact, frequent and rapid changes in load requirements during the daily operation of the vehicle may lead to increased stress on PEMFC due to pressure oscillation and oxygen starvation, resulting in decreased life span [43]. Furthermore, power is irreversible when PEMFC is used alone. In order to provide peak power in time and improve energy recovery, HPS is indispensable. And the optimal control strategy proposed for HPS improves the system cost, hydrogen consumption and safety by coordinating FC and auxiliary energy [87,88]. 2.1. The mechanism of PEMFC Fig. 4 shows the reaction mechanism of PEMFC. FC reactor is the main component of FC system, including electrode, proton exchange membrane (PEM), bipolar plate, gas diffusion layer, end plate and other components. The basic principle is the reverse reaction of electrolytic water, and the specific process is as follows: 1) Hydrogen reaches the anode through the pipe or gas guide plate; 2) Under the action of the anode catalyst, 1 hydrogen molecule dissociates into 2 hydrogen protons and releases 2 electrons. The anode reaction is as [89]

H2 → 2H + 2e−

(1)

3) At the other end of the battery, oxygen (or air) reaches the cathode through the pipe or gas guide plate. Under the action of the cathode catalyst, oxygen molecules and hydrogen ions react with electrons arriving at the cathode through the external circuit to generate water. The cathode reaction is as [89]

1.5. The research contents of this paper In conclusion, many aspects need to be considered in the research of HEV, especially power distribution, which is an important part. In FCHEV, EMS determines the power supply of FC and battery or SC. The performance of EMS determines the cost, energy consumption and

2H +

1 O2 + 2e− → H2 O 2

(2)

Fig. 5 is a schematic diagram of a typical PEMFC power supply system [30]. The system is composed of ontology and its subsidiary 5

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system. The durability of FC is the key of FCHEV. A large number of studies show that the key factors affecting the life of fuel cell include dynamic condition, start-up, continuous idling and so on, which are ultimately determined by the system control [75,78]. Therefore, fuel cell system control technology has become one of the most critical technologies of FC. Due to the characteristics of voltage and current output of FC and the hysteresis of dynamic response, the HPS with fuel cell and auxiliary power was developed by combining the principle of HEV. Fig. 6 is a schematic diagram of PEMFC-HPS and its energy management process. HPS mainly includes FC, auxiliary power source, converter and energy management controller. As the main power source of the vehicle, FC provides most of the energy for the normal operation of the vehicle. The auxiliary power source is the battery or SC, which can provide the power required by the vehicle under special conditions such as starting, accelerating and climbing. In this way, the energy required by the vehicle in different working conditions can be guaranteed, and the impact on the FC can be reduced to improve the life of the fuel cell. Fig. 4. The reaction mechanism of PEMFC.

2.2. The structure of HPS According to the type of auxiliary power used in the system, HPS of mainstream fuel cell vehicles in the market can be roughly divided into three types: 1) PEMFC + battery 2) PEMFC + SC 3) PEMFC + battery + SC The driving power comes from PEMFC and lithium battery pack or SC. However, due to the mismatch between the output voltage of PEMFC and the auxiliary power supply, it usually needs to be controlled by DC/DC converter. A single DC/DC converter is used to control the power output of PEMFC, and the bidirectional DC/DC converters are used to control the charge discharge power balance of auxiliary power supply to maintain the voltage stability of HPS. The structure of storage system affects the EMS design focus, and different HPS design strategies have a certain gap in performance. The above strategies are classified and compared according to HPS. The results are shown in Table 2.

Fig. 5. The Schematic diagram of a typical PEMFC power supply system [30].

Fig. 6. A schematic diagram of PEMFC-HPS and its energy management process. 6

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Table 2 The HPS performance comparison. HPS

Refs

Characteristics

FC + battery

[90–95]

FC + SC

[7,96–99]

FC + battery + SC

[100,101]

In the acceleration process, the system uses the battery as the peak power supplement, and absorbs the recoverable energy in the braking process. However, the voltage of FC does not match that of lithium battery, so it is usually connected by power converter. Another significant disadvantage of batteries is that their life cycle is extremely limited. When PEMFC is underpowered, SC provides the energy needed. SC can be charged and discharged at high speed, but it cannot be used for battery charging, so it cannot effectively recover the recycled energy. In addition, the price of SC is higher than that of battery, so the cost is a factor to be considered in the system adopting SC. It is more reliable than single power system because it concentrates the advantages of high energy density of battery and high-power density of SC. However, it costs more.

1) Inheritance 2) Variation 3) Survival struggle

2.3. Energy management optimization control The essence of optimization problem is an extremum problem. Almost all optimization problems can be summed up as follows: given a set (feasible set) and a function (objective function) on a set, the extremum of the function on the set is obtained. Advanced intelligent algorithm seems to be the best way to solve this problem. At present, the optimization methods of FCHEV-EMS mainly include PID, linear programming, dynamic programming, Pontryagin’s minimum principle, evolutionary algorithm, etc., which are summarized in Table 3. Among them, GA with parameter optimization function has become a choice for researchers to improve existing strategies. GA is a powerful and feasible method, which can effectively point to global optimization, but it is not easy to fall into local optimization. Compared with other evolutionary algorithms or population algorithms, GA has the advantages of uniform population movement, mature convergence rules and so on, which can easily deal with discrete problems. Then it introduces the algorithm flow of GA and the realization of single objective or multi-objective optimization.

GA introduces the above principle of natural selection and survival of the fittest into the coding series population formed by the optimization parameters, and then judges the individual character according to the fitness function set by the research goal as [114]

⎧ min J = f (x1, x2, ⋯, x n ) ⎨ s. t . g (x ) ⩽ 0 ⎩

(3)

where x1, x2 , ⋯x n are the parameters to be optimized; J is the fitness; f (x ) is the fitness function, g (x ) is the constraint function. A set of solution vectors can be obtained by calculation. Then, individuals are screened by means of reproduction, hybridization and genetic variation to retain adaptive individuals and form new populations. The new generation not only keeps the information of the previous generation, but also inherits the gene probability of the excellent individuals. In this way, before the termination condition is reached, the adaptability of individuals in the group is constantly improved. The basic flow of traditional genetic algorithm is shown in Fig. 7.

3. The principle of GA As an optimization method, GA normally has many interesting applications, such as path finding problem, prisoner’s dilemma, motion control, center finding problem, TSP problem, production scheduling problem, artificial life simulation, even energy strategy which is used in this paper, etc. This section briefly introduces the basic principles of GA and the improved method when dealing with multiple objects.

3.2. The Multi-objective optimization method In the traditional GA fitness function mentioned above, there is only one optimization objective. However, in the actual EMS optimization process, researchers have many goals, such as reducing energy consumption, extending battery life and so on. In the case of multi-objective, not all objectives have an optimal solution, and it is impossible to simply compare objectives. It needs to improve the traditional genetic algorithm. We can usually set different weights according to the importance of each objective, and transform the multi-objective optimization problem into a simple single objective optimization problem, as

3.1. The mathematical mechanism of the primitive GA The primitive GA is based on Darwin's theory of natural selection. The theory of natural selection includes the following three aspects: Table 3 The optimization method and its characteristics generally adopted at present. Optimization techniques

Paper

Advantage

Disadvantage

PID

[102]

Easy to control, simple structure

linear programming Dynamic programming

[103] [104–106]

Reduce the cost of basic methods Improving solution efficiency with knowledge and experience

Pontryagin’s minimum principle PSO

[107,108] [109,110]

Considering the degradation maximum power and efficiency Fast optimization

Rule-based logic optimization

[111]

Simple and easy to implement

Other standalone optimizations

[112,113]

GA

[93,99]

Novel structure, Less hydrogen consumption Not easy to fall into local optimum; Good convergence; Easy to multi-objective optimization

7

High battery stress; Total operating cost Research limited to simulation Limited to simulation; Calculation collapse caused by dimension problem Without obvious simulation tools; Unmarked simulation tools; Loss not considered; Only optimize limited energy storage system; Limited to simulation; Unmarked simulation tools; Battery life not considered Limited to simulation; Low practicability Limited to simulation

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Fig. 7. The basic flow of GA.

[114]

T ⎧ min J = [f1 (x1, x2, ⋯, x n ), f2 (x1, x2, ⋯, x n ), ⋯, fk (x1, x2, ⋯, x n )] ⎨ s. t . gi (x ) ⩽ 0, i = 1, 2, ⋯, p ⎩

⎧ min J = ω1 f1 (x1, x2, ⋯, x n ) + ω2 f2 (x1, x2, ⋯, x n )+⋯+ωk fk (x1, x2, ⋯, x n ) ⎨ s. t . gi (x ) ⩽ 0, i = 1, 2, ⋯, p ⎩

(5)

NSGA-II adopts the fast non dominated sorting algorithm, which greatly reduces the computational complexity and maintains the diversity of the population, thus preventing the loss of the optimal individual and improving the speed and robustness of the algorithm.

(4) where f1 , f2 , ⋯fk are objectives of optimization; gi (x ) is the constraint function; ω1, ω2 , ⋯ωk are Corresponding weight, and the sum of weights is 1. When there are many constraints in the optimization process, penalty factors can also be set in a similar way, and the constraints can be considered in the fitness function. In addition, the more effective solution is NSGA-II. When solving multi-objective problems, variables, objective functions and constraint functions are the three elements of multi-objective problems. The fitness function is transformed into Eq. (5), which does not necessarily have the best solution for all objectives [101].

4. Energy management of FCHEV based on GA Compared with the pure FC power system, the biggest advantages of adding EMS to the HPS are as follows: 1) EMS can improve the response ability of power system to load sudden change during acceleration; 2) EMS can meet the peak power demand of power system. As a pure

Fig. 8. The classification of the EMS. 8

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The GA has the characteristics of global optimization and is not easily trapped in local optimization. According to different optimization objectives, GA can be applied to parameter optimization to improve the results of the above optimization methods. In this paper, the optimization objectives and specific parameters of GA are reviewed and analyzed. Energy management strategies can be divided into fuzzy rule method, deterministic rule method and global optimization method, as shown in Fig. 8. GA is to improve the structure and parameters of these methods. The following section details this optimization process.

FC power system, it does not need to adjust according to the peak demand; 3) The energy management system limits the operation of fuel cell to an efficient work point, and improves the operation efficiency of fuel cell. The possibility of regenerative braking is improved, and the hydrogen economy is significantly improved. For HPS, the energy distribution strategy should be studied. Domestic and foreign scholars have done a lot of research on the control strategy of HPS and obtained many beneficial research results. For example, Zhang et al. studied an intelligent control strategy based on operation mode control [115]. Ates et al. proposed an EMS algorithm based on neural network and wavelet transform by combining the ability of wavelet transform to process transient signals and the monitoring method of neural network [54]. Different control strategies are proposed for high-power FCHV, such as mode control and equivalent fuel consumption [116,117]. Scholars design EMS for different purposes from different perspectives. Most of these strategies are suitable for reducing energy consumption. However, it is necessary to further improve protection measures, reduce costs and improve performance while reducing consumption. Many scholars have made in-depth studies on the optimization issue. Bizon et al. presented an implementation optimization strategy for controlling the flow velocity of FC system by using load tracking technology [118]. After that, he used DC bus load servo control loop and optimized control loop based on air flow global extremum search algorithm to improve fuel economy [119]. Sorrentino et al. used parameterization and heuristic methods, and took into account the combined cost and component weight model [120]. Melo et al. considered FC-HEV and FC-PHEV powertrain, and used single-objective and multi-objective meta-heuristic optimization method to optimize the target cost and fuel consumption minimization [121]. In the mathematical sense, the optimization method is a method to find the extreme value, even if the objective function of the system reaches the extreme value, that is, the maximum value or the minimum value, under a set of equality constraints as an equality or inequality.

4.1. Fuzzy rule-based EMS optimized by GA Fuzzy Rule-Based EMS is a rule-based method composed of Fuzzy logic. Fuzzy logic refers to the method of judgment and reasoning by imitating the concept of uncertainty in human brain. One of the advantages of fuzzy logic is that it can be adjusted to improve the degree of freedom of control. Therefore, fuzzy control can be used to design the EMS of FCHEV-HPS. But its disadvantages are also obvious. As mentioned above, fuzzy rules mainly come from expert knowledge or control experience, and there are inevitably defects of parameters or rules in manual setting of expert knowledge. Therefore, researchers usually combine intelligent methods with fuzzy logic to give full play to their characteristics. In this paper, GA is used as an optimization tool to select the most suitable parameters for the current system by setting fitness functions suitable for different purposes. For example, Alexandre et al. proposed a HEV control strategy based on FC and battery [90]. It uses GA to optimize the parameters of trapezoidal membership function in fuzzy control. The results show that the hydrogen consumption is reduced by 22% compared with the conventional fuzzy controller. However, this strategy has low practicability. The objective function only considers hydrogen consumption. For FC, reactant starvation occurs when the transport of reactant gas can't keep up with the amount of reactions under high current. In HESS, EMS should not only reduce the energy consumption of vehicles, but also achieve the goal of improving the stability of the system, extending the service life of components, improve dynamic performance and so

Fig. 9. The basic flow of EMS in [97]. 9

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4.1.1. Reduce the current fluctuation The fluctuation of current affects the service life of system components, especially energy storage components. Smoother input and output currents help extend battery life. Setting the appropriate fitness function can help balance current fluctuation and energy consumption. Therefore, Zhang et al. generalized the optimization objective of

on. The reduced physical quantity is the superposition of the whole running process. GA can do a lot more than reduce energy consumption. According to different optimization purposes, the following strategies are classified and summarized again. The following strategies are classified and summarized.

Fig. 10. Results of fuzzy EMSs for NEDC, UDDS and HWFET: (a) power distribution of the proposed fuzzy EMS, (b) current output of FC, (c) voltage output of FC, and (d) SOC of SC [97]. 10

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optimization algorithm reduces the changes of current and voltage, and reduces a certain amount of hydrogen consumption. Compared with the fuzzy EMS minimizing H2 consumption, the current and voltage changes are reduced by more than 20%, but a little more H2 is consumed. Obviously, the change of FC voltage and current in this method is greatly limited, but the cost is a slight increase in H2 consumption, especially in road congestion. The more crowded the road is, the more efficient the method is. The algorithm based on GA can effectively coordinate the minimum voltage and current change and hydrogen consumption, which greatly improves the safety and service life of SC. Recently, on the basis of the above research, Zhang et al. proposed a new EMS, which combines online identification mode with off-line optimization. The flow chart is shown in Fig. 11 [96]. According to the real-time driving mode, the energy management controller allocates instantaneous power to FC and SC and adaptively changes the output gain. In the first step, using neural network classifier to extract the available characteristics of historical speed data, the driving mode is divided into four different parts, and four different adaptive coefficients are obtained to realize the recognition of driving mode. The output of fuzzy energy management controller is adjusted adaptively according to different driving modes. Among them, the pattern recognition and energy management controller operate independently. So, GA was used to optimize the recognition effect of driving mode and the specific parameters of fuzzy rules controller. The objective of this EMS is to minimize both fuel consumption and current fluctuation. Therefore, the same constraints and objective functions are used to optimize the system. There are 16 fuzzy parameters and 4 adaptive coefficients of driving modes in the controller which need to be optimized. After off-line GA optimization, adaptive fuzzy energy management can be obtained. The experimental results in the Fig. 12 show that, firstly, the driving mode classifier can effectively identify various driving modes online, and the detection accuracy can reach 95%. Then, compared with the conventional fuzzy EMS, the selfadaptive fuzzy EMS optimized by GA reduces the hydrogen consumption by 8.89%, and the root mean square difference is small, which shows that it has smaller current and voltage fluctuations and prolongs the life of FC. Much like the case of the fuzzy EMS minimizing H2 consumption, SC provides more power to reduce fuel consumption. GA optimization of adaptive fuzzy controller is still very good, but unfortunately, although the driving mode classifier can correctly identify four driving modes online, the adaptive coefficients are very similar in the case of Metro and urban roads, and the actual effect of driver identification needs to be further verified.

balancing energy consumption and system security in the form of mathematical formulas as [97]

min J1 = ∑ m H2 , J2 = ∑ ΔI j 2 j

j

(6)

where J1 is the total amount of hydrogen consumption, and J2 represents the degree of current fluctuation. In addition, the energy storage system is composed of FC and SC, so meeting the power requirements will result in an equation constraint as [97]

Pdem = Psc + Pfc

(7)

where Pfc is the power required by FC, Psc is the power required by SC and Pdem is the power demand. Based on the combination of fuzzy controller and low pass (LP) filter, an energy management system is proposed. They use the improved GA to solve the bi-objective optimization problem of the fuzzy control system, and improved the parameters of the fuzzy rule base and membership function. As shown in Fig. 9, Uf, Pdem and SOC are changed into the fuzzy domain [0, 1], Pdem and state of charge (SOC) as fuzzy inputs, and Uf as fuzzy outputs. The Gauss function is defined as the input and output fuzzy function. In addition, LP filters are also used to limit the transient peak value of the output power of FC during start-up and sudden acceleration. The output power of FC is revised as [97]

Pfc (k ) = k1 Pfc (k − 1) + (1 − k1) uf Pdem

(8)

where k1 is the time constant that affects the output flatness. They minimize FC current disturbance, reduce fuel consumption, satisfy FC and SC constraints, and optimize time constants affecting output flatness, center and width of Gauss MFs, and rule base of the fuzzy energy management controller with improved GA. So, the objective function is rewritten as [97]

min J = ω1 J1 + J2 + JP1 + JP2

0.5 - SOC , SOC < 0.5 JP1 = ⎧ , J = |Pdem − (Pfc + Psc )| SOC − 0.9, SOC > 0.9 P2 ⎨ ⎩

(9)

(10)

where ω1∈(0, 1) is the weighting coefficient that will affect the optimization results and is set by trial and error. The range constraints of SOC and the difference between the required power and the actual power supply are taken as penalty factors and added to J to calculate the optimal solution. The driving cycles of NEDC, UDDS and HWFET are simulated using MATLAB and ADVISOR, and the results are compared with the fuzzy EMS minimizing H2 consumption and the expert fuzzy EMS. The results are shown in Fig. 10, and the specific data are shown in Table 4, where RMSE is the mean square error of voltage and current and represents its variation degree. Compared with the expert fuzzy EMS, this

4.1.2. Improve the dynamic response For a vehicle energy control system, the dynamic performance is very critical, it ensures the normal operation of the vehicle, but also determines the merits of the system. The main indexes can be divided into two characteristics: accuracy and rapidity. Accuracy refers to

Table 4 Comparison of simulation results for three traffic conditions [97]. Cycle

Parameter

Proposed fuzzy EMS

Fuzzy EMS Min H2

Expert fuzzy EMS

NEDS

Hydrogen consumption(kg) RMSE(I, V) proportion of FC supplied proportion of SC supplied

0.2 (1.96, 1.69) 55% 45%

0.18 (2.31, 2.07) 51% 49%

0.22 (2.41, 2.32) 64% 36%

UDDS

Hydrogen consumption(kg) RMSE(I, V) proportion of FC supplied proportion of SC supplied

0.22 (1.73, 1.63) 56% 44%

0.2 (2.85, 3.31) 53% 47%

0.24 (2.98, 3.31) 65% 35%

HWFET

Hydrogen consumption(kg) RMSE(I, V) proportion of FC supplied proportion of SC supplied

0.16 (3.35, 3.05) 59% 41%

0.14 (4.15, 4.15) 63% 36%

0.21 (4.66, 4.81) 63% 37%

11

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Fig. 11. The basic flow of Energy optimization management in [96].

of the car still needs good EMS support [122]. In previous studies, many articles have considered the problem of dynamic response, which usually adopt PI control and adaptive fuzzy rule control. PI controller improves the system's quick response ability and frequency conversion operation ability under instantaneous state by bidirectional boost. Adaptive fuzzy control keeps FC in an efficient field and thus reduces hydrogen consumption. For example, in Ref. [123], passive control was used for system monitoring. In Ref. [124], fuzzy control based on three kinds of data fusion was used to provide energy management for online drive cycle. The results show that it can run more stably and has higher dynamic response. In Refs. [125,126], fuzzy control ensures good FC operation and gave the dc bus

whether there is a deviation between the power required by the system and the power actually provided, that is, the accuracy of the response. Rapidity is the rate of energy supply, including maximum speed, acceleration, climbing ability and so on. In the hardware architecture of the FCHEV, SC is the first choice for improving dynamic performance. When the power suddenly changes and the braking energy is restored, PEMFC system can achieve rapid dynamic response simply with SC. But SC is not everything, it can only be used as an auxiliary energy supply in the phase of state mutation. In general, the system needs to meet the three constraints: power balance, good SC and FC coordination, and FC current to meet the requirements. The dynamic response of FC has an important impact on the overall drive, that is, the long-term operation

Fig. 12. FC power and current output [96]. 12

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To solve the power allocation problem of FCHEV, these two inputs can’t only satisfy Pdem, but also maintain battery SOC, and the sudden increase of fuel cell power supply should be avoided. These strategies constitute a fuzzy rule base. In this study, the fuzzy and defuzzy MFs have a common structure, which is composed of triangular membership. However, there is a trade-off problem in the determination of SOCref , because the high SOCref , results in the increase of fuel cell utilization, so it often operates in low efficiency areas. Otherwise, the utilization of batteries will increase, and the batteries may run out during the journey. Therefore, SOCref should be optimized to achieve low fuel consumption and battery maintenance. The GA is used to optimize the variables of the fuzzy controller, i.e. the parameters of input and output MFs and SOCref . The fitness function is [93]

satisfactory dynamic characteristics. However, most studies are worth improving again. They ignored the problems of total cost and global optimization, and GA can add these factors into the fitness function. The result summarized in this section makes use of GA optimized fuzzy rule controller to achieve the purpose of reducing the deviation value between the power required by the system and the power actually provided, and dynamic characteristics are improved from the side. Reducing the output deviation helps to provide power more accurately and further improve the dynamic response. Caux et al. also proposed a fuzzy reasoning system based on GA optimization [99]. The energy management problem was described as a global dynamic optimization problem with constraints. Hydrogen consumption is quantified as a cost function and minimized. Eq. (7) also needs to be satisfied here. The cost function of the minimization is the total consumption of hydrogen in the driving cycle as [99]

min J =

Pfc (j )·Δt

min J1 = ∑ m H2 = ∑ η j

j

fc (Pfc (j ))

m

∫ m Htar2 + ω2 H2

(

|ΔSOC| 2 ⎤ |ΔSOC|tar

)⎦

(14)

where, is the minimum target value of hydrogen consumption, |ΔSOC|tar is the minimum target value of the absolute difference between the initial SOC and the end SOC, and the sum of weight factors is equal to 1. According to the formula, the fitness function reflects the influence of hydrogen consumption and SOC deviation. The two driving cycles used in the simulation process are HWFET and UDDS. The standard deviation of optimized UDDS in battery power and SOC is larger than that of HWFET cycle group in Fig. 13. The results show that the EMS is still reliable. But since the optimization results may vary with the driving cycle of the application, it is impossible for an optimization result to cover all the driving cycles.

(11)

4.1.4. A comprehensive strategy The previous schemes combined the primary goal of reducing energy consumption from the aspects of current fluctuation, dynamic response and battery SOC, and optimizes the original EMS item by item. They focused on solving the individual problems targeted by researchers, but were not comprehensive. There are still many aspects in EMS that deserve further optimization. Therefore, this section introduces a more comprehensive fuzzy strategy. The author considers the energy consumption, component life, dynamic response and even system efficiency, and comprehensively optimizes the original fuzzy rule controller with GA. The more points are considered, the more complex the objective function is, but the wider its scope is. Saman et al. proposed a more comprehensive unconstrained objective function for the fuzzy controller [7,98]. Its main objective is to minimize the consumption of hydrogen, and the corresponding weight is large. Other objects, such as the average efficiency of FC and energy storage, the difference between required and achieved speeds, and the value of SOC at the end of the cycle, have lower weights. Its objective function is as [98]

(12)

where equadr is the average quadratic error of power. The performance of the optimized fuzzy system in two different driving cycles are tested. The results shown in the Table 5 illustrate that the performance of the optimized fuzzy system are better than those of the unimproved algorithm in two driving cycles. In addition, its robustness is also improved. 4.1.3. Reduce the SOC deviation Similar to reducing current fluctuations, reducing the depth of charge and discharge is also a factor affecting battery life. The number of times a battery can be charged depends on the depth of its discharge. The product of chargeable times and discharge depth is equal to the total number of charging cycles completed, and the higher the total number of charging cycles completed, the higher the battery life. Therefore, we can use the objective function of GA to select the strategy to lower the discharge depth of the battery, namely the initial value and final difference of SOC, to extend the battery life. In another study, Ryu et al. introduced an EMS with FC and battery, and proposed a fuzzy controller based on GA optimization [93]. The bus voltage and SOC of battery are used as input signal of controller, and duty cycle controller adjusts DC-DC converter. The duty cycle controller generates a duty cycle to track the required FC current. The difference from the reference values (bus voltage and SOC) of each control target state can provide information about the high or low current state, i.e. the fuzzy input is [93] ref eSOC = SOCref − SOC , eV = Vbus − Vbus

⎣

m Htar2

where Δt is the sampling time and ηfc (Pfc (j )) is the efficiency of the corresponding Pfc. Fuzzy rules are established to manage the power of fuel cells and the energy state of storage cells. Two input variables, SOC and Pdem, are still used in the implementation of the fuzzy decision system, and the output variable is the power Pfc provided by the fuel cell. The general idea behind these rules is that the power provided by the fuel cell stacks is as high as the power demand and/or as low as the charging state of the storage unit. In addition, when the power demand is low or the charging state of the storage element is high enough to provide the power, the FC can provide the lowest possible power. The parameters of the fuzzy system are fine adjusted by GA. In this case, the goal is to minimize hydrogen consumption while ensuring the power required for the entire vehicle cycle. So, the fitness function is shown in Eq. (12), which takes into account the total hydrogen energy consumption in the driving cycle and quantifies the average quadratic error between the required power and the actual power supply [99].

min J = ω1 J1 + equadr

1 ⎡ω1 ω1 + ω2

J = ω1

m

¯ + ω5 |Δv| ∫ m Htar +ω2 effFCS + ω3 effES + ω4 ΔSOC |Δv|tar 2

(15)

H2

where, effFCS and effES are the average efficiency of fuel cell system and ¯ is the corresponding penalty energy storage system respectively; ΔSOC value of Δsoc , calculated by Eq. (16); |Δv|tar is the minimum target value of absolute difference of velocity [98].

⎧1 − 10ΔSOC , 0 ⩽ ΔSOC ⩽ 0.1 ¯ = 1 + 10ΔSOC , − 0.1 ⩽ ΔSOC < 0 ΔSOC ⎨ 0, other ⎩

(16)

Table 5 Comparison of simulation results for two traffic conditions [99].

(13)

ref is the reference where, SOCref is the reference value of SOC and Vbus value of bus voltage.

13

Driving mode

FL

GA-FL

Improvement

Suburban area Tramway line

10866kWs 33358kWs

8359.9kWs 29802kWs

23.1% 10.6%

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Fig. 13. The comparison results of optimized sets for HWFET and UDDS [93].

among fuel economy, power supply efficiency and SOC change, so the optimized fuzzy controller can be used as a more suitable control strategy. In the process of improving EMS, the goals to be optimized and their priorities should be carefully considered. In addition to pursuing low energy consumption, more attention should be paid to performance factors. The objective function of GA is determined by the optimization objectives and criteria of researchers, and the setting of objective function is a key step in the optimization process. In the EMS of fuzzy control, the main task of GA is to optimize the center and width of the fuzzy membership function through the collected data, and even improve the fuzzy rule table. GA effectively coordinates the goals of reducing hydrogen consumption, extending the service life of components or improving dynamic response. Compared with the traditional fuzzy EMS, the consumption of hydrogen is reduced, and the stability of voltage and current is also improved. The EMS performance comparison of fuzzy rules based on GA optimization is presented in Table 7.

In addition, the results of driving performance and test procedures are considered to be constraints to be met. Because the traditional GA is used for unconstrained optimization problems, the penalty function deals with constraints by reducing the fitness value. The unconstrained fitness function can be updated to [98]

min J ′ = J −

∑ ρj hj 2 − ∑ βj gj 2 j

j

(17)

In the equation, hj and gj are equality constraints and inequality constraints respectively. These functions are obtained by dynamic constraints and weighed by driving performance. The penalty factor ρj and βj is determined by [98]

0, gj ⩽ 0 ρj = ρ > > 0, βj = ⎧ ⎨ ρ , gj > 0 ⎩

(18)

After solving the equations, the effectiveness of GA in fuel economy is verified and evaluated by ADVISOR software. EMS optimization performance is evaluated by five drive cycles. Table 6 shows a summary of the results obtained by five different EMS in a given drive cycle, with the best values shown in bold. It can be seen from the table that the SOC value of the optimization strategy at the end of the cycle is almost equal to the initial SOC value, and the optimization results of hydrogen fuel and mixed fuel economy are obtained. They achieve a good balance

4.2. Deterministic rule-based EMS optimized by GA The rule-based control strategy can easily realize the real-time monitoring of the HPS. Most of these controllers are static and make decisions only for transient inputs. The working point of motor is determined by rule table or flow chart to meet the requirements of driver

Table 6 Results of each control strategy over the drive cycles [98]. Cycle

Parameter

ADV-PTC

OMC

FLC

Opt-OMC

Opt-FLC

UDDS

Hydrogen fuel economy (mpg) Hybrid equivalent fuel economy (mpgge) △SOC Fuel cell average efficiency (%) Battery average efficiency (%)

3.1 46.1 0.09 52.35 84.24

3.5 51.5 0.04 50.84 83.5

3.1 46 0.1 55.1 82.35

3.6 52.9 0.04 52.97 82.71

3.6 53.3 0.03 51.93 82.15

US06

Hydrogen fuel economy (mpg) Hybrid equivalent fuel economy (mpgge) △SOC Fuel cell average efficiency (%) Battery average efficiency (%)

4.3 63.1 −0.07 54.57 73.70

4.1 60.3 −0.06 54.95 73.55

3.4 50.6 0.01 56.29 73.07

3.5 51.4 0 55.93 72.99

4.6 67.7 −0.1 54.4 73.11

HWFET

Hydrogen fuel economy (mpg) Hybrid equivalent fuel economy (mpgge) △SOC Fuel cell average efficiency (%) Battery average efficiency (%)

5.6 82 −0.03 53.62 82.84

6.5 96.3 −0.08 53.88 82.33

4.1 60.2 0.08 56.17 82.30

4.9 72.7 0 54.51 82.83

6.9 101.7 −0.09 53.57 82.85

FTP

Hydrogen fuel economy (mpg) Hybrid equivalent fuel economy (mpgge) △SOC Fuel cell average efficiency (%) Battery average efficiency (%)

3.5 51 0.09 52.91 82.86

4.1 60.4 0 51.2 83.23

3.4 50 0.1 55.49 81.65

4.2 61.8 0 52.88 82.84

5.3 78.6 −0.09 51.91 82.02

ECE + EUDC

Hydrogen fuel economy (mpg) Hybrid equivalent fuel economy (mpgge) △SOC Fuel cell average efficiency (%) Battery average efficiency (%)

3.3 48 0.07 52.14 84.05

6.4 94 −0.08 53.25 81.84

3 43.7 0.1 54.67 82.11

4.1 60.3 0 51.76 81.95

7 103.7 −0.09 52.46 83.29

14

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Accurate identification of driving patterns, but it’s difficult to resolve complex situations. Robustness is better, but there is no effective energy recovery. Minimize hydrogen consumption and current fluctuations

The transparency of the experiment is low and further verification is needed. The objective function is considered comprehensively and transformed into an unconstrained problem, but the overall dynamic performance is low.

Too few factors are considered, so practicality is low. It has to do with road conditions, the more crowded, EMS efficiency is higher. Minimize hydrogen consumption Minimize hydrogen consumption and current fluctuations

Fig. 14. The basic EMS flow in [100].

and battery in the most effective way. Moreover, the strategy based on deterministic rules has the highest practicability. However, deterministic rules and fuzzy rules have the same defects. Both of them are designed on the basis of expert knowledge, and human factors have a great impact on the strategy. However, GA makes up for this problem by finding out the most appropriate result through complete calculation, improving the artificial set parameters and optimizing the deterministic rules. According to the different optimization objectives, each case is described separately.

4.2.1. Balance the power output EMS based on deterministic rules not only reduces energy consumption, but also has optimization objectives. In HPS, sometimes the strategy can't work according to the actual design, some components may be overloaded, and the utilization rate of other components is far lower than expected. This problem can be improved by setting special GA objective function, too. Li et al. proposed an EMS using GA to optimize deterministic rule-based parameters [100]. In this paper, an EMS based on simple rules is set up to distribute power. The energy system consists of FC, SC and battery. The power distribution process is shown in Fig. 14. The simulation results which are shown in Fig. 15 are not ideal, and the simulated hydrogen consumption is 3.3469 kg. Although EMS can basically meet the energy distribution of train operation, FC has not been shut down in the whole working state, and the maximum output power is 170 kw. In the traction stage of vehicle, the energy output and energy absorption of storage battery is very small, so the regenerative braking energy is not effectively utilized. In the braking stage, regenerative braking energy is not effectively recovered, resulting in huge loss of renewable energy. So, it is necessary to optimize its dynamic system. It can be seen that in the process of EMS operation, the maximum output powers of FC, battery and SC determine the efficiency and reliability of EMS, so this party uses GA to optimize these three parameters. The objective function is based on the amount of hydrogen consumption. The SOC deviation of battery and SC is transformed into equivalent hydrogen consumption, and different weight factors are set. The formula is as [100]

Fuzzy + NN + GA

Fuzzy + GA

Fuzzy + GA Fuzzy + GA

[96]

[99]

[93] [7,98]

Membership function parameters Membership function parameters

Fuzzy + GA Fuzzy + GA [90] [97]

Membership function parameters Membership function parameters, fuzzy rules and a time constant influencing the flatness degree of the output. Membership function parameters and the adaptive coefficients of the driving mode Membership function parameters

Minimize hydrogen consumption and mean square error between the required power and the actual power Minimize hydrogen consumption and SOC deviation Minimize hydrogen consumption, efficiency of the system, SOC and speed deviation

Advantages/Disadvantages Optimization objective Optimized parameter Method Refs

Table 7 Fuzzy rule-based EMS performance comparison.

X. Lü, et al.

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Fig. 15. Output power of FC and SOC of SC and battery in unoptimized EMS [100].

min J = ω1 ∑ m H2 + ω2 k2 |ΔSOCsc| + ω3 k3 |ΔSOCba| j

of battery comes from the energy exchange with SC. Energy exchange is bidirectional, that is, the battery can charge the SC, and the SC can charge the battery, which is completely redundant. Odeim et al. discussed a real-time energy management of FCHEV based on the proportional integral (PI) controller [101]. As shown in Fig. 17, two kinds of EMS are introduced. The first strategy S1 (Figure a) is used as a benchmark to illustrate the advantages of the second strategy S2 (Figure b). Both strategies use batteries to regulate the state of supercapacitors by a proportional controller, and fuel cell systems to regulate the state of batteries by a PI controller. However, compared with S1 strategy, the biggest advantage of S2 strategy is to prevent unnecessary power switching in HPS, thus significantly reducing the battery load and making better use of the advantages of SC. The design of power management strategy is simplified to the selection of coefficient parameters in PI: Kp.sc, Kp.ba and Ki.ba. The selection criteria are as follows: (1) minimizing hydrogen consumption; (2) Supercapacitors are responsible for most of the transient power and minimizing the contribution of batteries, i.e. the objective function is [101]

(19)

where k2 and k3 are the proportion coefficients of equivalent energy consumption. After determining the optimization objective and the fitness function of the optimization objective, the energy allocation strategy of the tram is optimized by using GA. After running the simulation, in the 36th generation, the algorithm converges, and the energy consumption of the whole trip is equivalent to 2.8354 kg. The results of Fig. 16 show that the maximum output power of FC is 101.21 kW, the battery is 91.57 kW, and the SC is 153.74 kW. The maximum output power of FC decreased from 170 kW to 101.2 kW, which never stopped during the whole operation. The cost of SC module is reduced from 3 parallel to 2 parallel. In terms of batteries, the utilization rate of batteries is greatly improved by outputting and absorbing more energy. The equivalent energy consumption of the whole line simulation is 2.8354 kg, which is 15% lower than the original strategy. GA improves the original power system from the perspective of battery utilization, energy recovery and cost. The strategy is relatively simple and easy to implement, but the overall transparency is not high, and its effectiveness deserves further verification.

J1 = ∫ m H2 (t ) dt , J2 =

1 T

∫ |Pba (t )| dt

(20)

The GA toolbox in MATLAB is used to calculate the Pareto frontier, which shows that there is a compromise between the two objective functions. The tradeoff is that increasing the contribution of the battery reduces the contribution of the SC, thereby reducing the consumption

4.2.2. Avoid power cycle between auxiliary sources In HPS with parallel battery and SC, sometimes most of the energy

Fig. 16. Output power of FC and SOC of SC and battery in optimized EMS [100]. 16

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Fig. 17. The PI strategy in [101].

in the energy supply, and the average power of S2 battery decreases by 90%. In deterministic rule-based EMS, the determination of initial rules is particularly important, which is the key to implement the upper and lower performance limits of EMS. The performance of deterministic rule-based EMS optimization by GA is shown in Table 8.

of hydrogen. At the same cell contribution rate, the hydrogen consumption of S2 is lower than that of S1. As shown in Fig. 18, simulation experiments verify the results. The driving cycle used is Manhattan's top speed of 40 km per hour. The simulation results are consistent with the experimental results, which verify the correctness of the simulation results: the contribution rate of battery and SC is negatively correlated

Fig. 18. Experimental evaluation of the power management strategy S1 and S2 over Manhattan [101]. 17

Energy Conversion and Management 205 (2020) 112474 This strategy is simple and easy to implement. The optimization effect is remarkable, but the system is complex. Minimize hydrogen consumption and SOC deviation Minimize hydrogen consumption, battery power and avoid power return

4.3. Global EMS optimized by GA The strategy based on global optimization is used to find the global solution of the most appropriate problem, which is widely used in the application of HEVs. It can be used to optimize irregular problems. For example, the adaptive structure of neural network (NN) makes it suitable for any control application, including EMS of HEV. A well-designed network can constantly adjust itself to update data through training. Particle swarm optimization (PSO) is also a meta heuristic algorithm, which can search for very large candidate solution space. The improved PSO-EMS can better determine the energy flow direction and flow rate of HEVs. The fuel consumption minimization problem is transformed into a convex nonlinear optimization problem, which is solved by linear programming. In principle, since GA is also a global optimization algorithm, all the strategies proposed in this paper belong to the global optimization strategy. However, we mainly consider the role of GA and optimization, so we classify according to the original method. This section introduces the strategy that the original method is the optimization method. Consistent with the previous two sections, the strategy is subdivided according to different optimization purposes. 4.3.1. Minimize hydrogen consumption In order to improve the economy and practicability of FCHEV, reducing hydrogen consumption has been the primary optimization objective of many EMS, and the weight of this objective in the fitness function is also very high. The global algorithm is the perfect choice for continuous energy reduction. NN is based on modern neuroscience, biology, psychology and other disciplines. At present, BP learning algorithm is widely used in NN training methods, but it has a series of shortcomings, such as slow convergence speed, the existence of local minimum value, poor global search ability, lack of dynamic characteristics and so on. However, GA is suitable for the optimization research and training of NN connection weights. It can combine the extensive mapping ability of NN with the global optimization and self-learning ability of GA, so as to improve the performance of control system. As shown in Fig. 19, XIE et al. proposed a management strategy for battery and FC [91]. The energy flow during operation is analyzed, and the energy management problem is transformed into a constrained neural network optimization problem. Neural network optimization controller uses three inputs and one output form: Pdem, ePdem of sampling time variation of Pdem and SOC of battery. The output power of DC-DC converter is Pdc, which can be equivalent to Pfc. The control accuracy and response speed are combined and number of hidden layers is set to 8, so the structure of NN controller is 3:8:1. The key of NN design is to select appropriate connection weights and thresholds. Therefore, GA is used to optimize 32 weights and 9 thresholds. The fitness function takes the fuel economy index per unit time as the performance value function. Then, according to the process of GA, under the condition of satisfying the system constraints, the optimization of weights and other parameters of the NN controller by GA is completed. The SOC-based fuzzy control strategy, load power and NN optimization control are embedded in the whole simulation system. The equivalent fuel consumption per 100 km is calculated by UDDS driving cycle. Compared with the traditional fuzzy control strategy, the energy consumption is reduced by 7.2%. In summary, the NN control strategy based on GA parameter optimization can reduce vehicle energy consumption and maintaining vehicle dynamic performance basically. Unfortunately, the experimental results only focus on 100 km equivalent fuel consumption, and have a lot to do with vehicle status. Compared with the fuzzy control strategy, the energy consumption of the vehicle even increases in the acceleration time of 0–80 km/h. In another study, SOC objectives were selected as parameters to optimize by Ribau et al. [94]. The SOC target can be regarded as the time correlation vector of the SOC value of the time step of each driving

Rule + GA PI + GA [100] [101]

The maximum output power of FC, SC and battery The proportional integral coefficient of the controller

Advantages/Disadvantages Optimization objective Optimized parameter Method Refs

Table 8 Deterministic rule-based EMS performance comparison.

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Fig. 19. The basic flow in [91].

The goal is to find a vector SOCt that results in a specified driving cycle with minimal fuel consumption. Objective function is designed as equation (19) [94].

Table 9 Performance of the reference and optimized vehicle [94]. Vehicles

Fuel consumption (MJ/km) ETC

OportoDC

LisbonDC

Max speed (km/h)

10.50 8.57

26.40 18.11

23.50 15.91

88.1 135.7

Acceleration time (s)(0–50 km/h)

10.2 7.7

min J1= ∑ m H2 (SOCt ) t

ICE vehicle Optimized FCHEV

In the process of GA optimization, the maximum permissible power supply of battery and FC must be considered, and penalty factors must be added. SOC objective is obtained by GA optimization. The simulation results are shown in Table 9, and compared with traditional ICE vehicles on three drive cycles. The optimized FCHEV has an average energy consumption of 27.3% lower than the ICE, and can reduce energy consumption by up to 32.3%. The ETC driving cycle shows less

cycle as [94]

SOCt = [SOCt = initial, SOCt = initial + 1, SOCt = initial + 2, ⋯, SOCt = final]

(22)

(21)

Fig. 20. The basic flow in [92]. 19

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Fig. 21. Best solutions achieved regarding FCHEV and FCPHEV optimization in ETC and PortoDC driving cycles [92].

Fig. 22. In terms of cost and fuel minimization, the fuel consumption of Porto driving cycle is compared [92].

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Fig. 23. The basic flow of EMS in [95].

where, mco2 is the equivalent greenhouse gas emissions in the process of vehicle production and use. In [92], NSGA-II was used for multi-objective optimization as Fig. 20. The optimization results show that there is a conflict between FC vehicle cost and fuel consumption (as well as LCI). Higher capacity batteries allow lower fuel consumption, but increase costs. Cost and energy consumption can also be concurrent goals; therefore, reducing demand for energy consumption leads to higher-cost vehicles. The multi-objective optimization diagram is shown in Figs. 21 and 22, and CTG refers to the energy consumption and CO2 emission of materials used in vehicle production, assembly and recycling. To better understand the differences between the solutions for each goal, Fig. 21 further analyze the LCI solution and powertrain costs for the best solution. In terms of LCI emissions, the CO2 emissions of the FCHEV bus are 16% lower than that of the fuel cell plug-in hybrid electric vehicle (FCPHEV). The optimized FCHEV powertrain is smaller than the FCPHEV battery, achieving a minimum cost of two drive cycles, which is reduced by 67% and 76%, respectively. In cost minimization optimizations, CTG has less impact than other goals when components are scaled down. The strategy can reduce energy consumption by 58% and emit 67% less greenhouse gases than traditional ICE. Later, Ribau et al. studied further, emphasizing the conflict between the importance of objectives and decision-making considerations in the final solution, and propose a solution as shown in Fig. 23 [95]. The goal is to optimize the power assembly to minimize costs and emissions. The multi-objective NGSA-II algorithm was still used to solve the pareto problem, and three decision methods were used to determine the best alternative technology. The global criterion method chooses the optimal FCHEV bus as the most suitable one. On the other hand, the pseudo-weight vector method can calculate the tradeoff between objectives and quantify the importance of each objective for each optimal solution. Generally speaking, the optimization effect is ideal. Global EMS based on GA optimization is not much, mainly focusing on reducing energy consumption and LCI. However, the author believes that this is a solution worth further study, especially in reducing complexity. The performance comparison of global EMS is shown in Table 10.

energy improvement potential than other driving cycles, with the lowest energy consumption reduction. This optimization method is not only an off-line optimization method, but also has great improvement in performance and energy consumption in the actual driving process. However, compared with ICE the cost has increased a lot, which is equivalent to converting the increased cost into energy saving, and the economy may not be worse in the long run. The results summarized in the next section calculate the life cycle impact, which takes hardware cost and energy cost together to obtain more thoughtful economic results. 4.3.2. Life cycle impact An important tool for assessing the impact of vehicle use is the life cycle impact (LCI) analysis. It simulates the cost, environmental impact, energy consumption and other data of the vehicle in the whole life cycle, and then conducts comparative analysis. Compared with simply examining energy consumption, LCI can better reflect the performance and influence of the vehicle. LCI and material life cycle analysis of FCHEV were made by Baptista et al. [127]. Bartolozzi et al. used LCI to evaluate and compare the environmental impacts of different schemes on hydrogen generation pathways, and to analyze the impacts of EVs [128]. Silva et al., on the other hand, carried out a detailed LCI of several alternative fuel nuclear power systems and summarized the impact of alternative technologies [129]. However, there are always some conflicts among the factors in LCI, such as hardware cost and energy consumption, which are negatively correlated. NSGA-II with multi-objective optimization function can just solve this problem, so this section introduces the research results of GA optimization LCI. Ribau et al. analyzed the significance of optimizing driving conditions and the conflict between investment cost optimization, efficiency optimization and LCI from the perspective of economy and emission [92]. Define the component cost function as [92]

min J0= ∑ Cfc + j

∑ Cem + ∑ Cba j

j

(23)

where, Cfc is the cost of fuel cell system, Cem is the cost of electric motor and controller, and Cba is the cost of battery pack. Like cost objectives, fuel consumption optimization also focuses on direct comparisons between different vehicle designs, aiming to minimize hydrogen fuel consumption. In addition, in order to reduce greenhouse gas emissions, LCI calculated the impact of vehicle energy utilization, energy production and vehicle manufacturing. The objective function of LCI is [92]

4.4. The summary of GA-based optimization parameters and objectives The above sections introduce the optimization process from the classification of EMS. The optimization object of GA in FCHEV-EMS is mainly the parameters appearing in the strategy, such as the width of membership function, the center point, the weights and thresholds in NN, the proportional integral coefficient in PI controller and so on. These parameters are the key factors to determine the performance of

min J2= ∑ mCO2 j

(24) 21

Energy Conversion and Management 205 (2020) 112474 Its utility is low. The computation is huge. The conflict between cost, energy consumption and emissions are considered. Minimize hydrogen consumption Minimize hydrogen consumption Minimize hydrogen consumption, cost and greenhouse gas emissions

the system. They control the timing of energy supply and storage. Hydrogen consumption is not only a necessary condition for optimization, but also a primary objective. It has a great weight in the objective function. But it is not the whole goal of optimization. The ultimate goal of each optimization is to make FCHEV have better performance and be accepted by the market. Therefore, it is necessary to study the factors such as battery life, cost and even driving comfort. It is not easy to balance these aspects. Fig. 24 shows the summary of optimization parameters and objectives. 5. Conclusion and challenges 5.1. Conclusion Due to the depletion of fossil fuels and serious pollution emissions, EVs are gradually replacing ICE vehicles as people's walking tools. Hydrogen as a fuel gas, the heat released by combustion is three times that of gasoline, and the product of combustion is water, which is completely pollution-free. The FC with hydrogen as the main fuel will become the most ideal energy in the 21st century. But the FC also has the disadvantages of high cost, limited hydrogen source, explosion and other security risks. With the development of technology, the HPS composed of FC, battery or SC will promote the progress of HEV with their respective advantages. In FCHEV, EMS determines the power supply of FC and battery or SC, the performance of EMS determines a series of issues such as the cost, energy consumption and component life of EV, so EMS is very important. Although there are EMS for FCHEV based on various methods, most of them are limited to initial power distribution. In this paper, aiming at the problem that the current EMS of FC-HPS has not enough consideration for dynamic performance, economy and durability, we take the unoptimized EMS as the prototype, study the optimization target of GA for its performance, and summarize the specific effect and object of optimization. According to the research purpose of this paper, the directions and challenges of FCHEV market in the future are summarized. 5.2. Future development of GA optimization application With the expansion of applications, GA has not only demonstrated outstanding results in the field of electric vehicle energy management, but also plays a very important role in fuel cell life prediction. In addition, there are several notable new trends in GA research: 1) machine learning based on GA. This new research topic extends the GA from the traditional discrete search space optimization algorithm to a new machine learning algorithm with unique rule generation function. This new learning mechanism brings hope to solve the bottleneck problem of knowledge acquisition and knowledge optimization and refining in artificial intelligence. 2) the parallel processing of GA research. This research is very important not only for the development of GA itself but also for the research of the new generation of intelligent computer architecture. 3) the penetration of GA and another new field of research called artificial life. The so-called artificial life is to use the computer to simulate the rich and colorful life phenomena in nature, among which the adaptive, and immune phenomena are important research objects of artificial life, and GA will play a certain role in this respect. 4) the combination of GA with evolutionary programming and evolutionary strategy. They are developed independently with GA almost at the same time. Like GA, they are also intelligent calculation methods to simulate the biological evolution mechanism in nature. In other words, they have similarities with GA and also have their own characteristics. At present, the comparative study and the discussion of the three are forming a hot spot.

NN + GA GA GA [91] [94] [92,95]

The connection weight and threshold of NN The target of SOC The cost of the battery pack and components

Advantages/Disadvantages Optimization objective Optimized parameter Method Refs

Table 10 Global optimization EMS performance comparison.

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The authors believe that with the continuous improvement of GA's 22

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Fig. 24. The summary of optimization parameters and objectives.

optimization performance, it will always have a place in the field of evolutionary computing.

5.3.2. Security and comfort Safety is now the biggest controversy over FCHEV. For FCHEV, hydrogen is the main energy source. As a clean fuel resource, hydrogen has great potential in the automotive energy supply market. However, hydrogen has high utilization value, and it is also flammable and explosive. Hydrogen and oxygen have a wide range of flammability and low ignition energy, which further increases their use risk. Every year, hydrogen explosion occurs in the laboratory, so the safety storage of hydrogen is the primary task when FCHEV is used. At present, the commonly used hydrogen storage device is a kind of special hydrogen storage tank. After the advent of FCHEV, the hydrogenation station should be planned, and its safe operation is more important. As for driving comfort, part of it comes from the dynamic performance of the vehicle, and the other part comes from the humanization of the vehicle design. This requires researchers to continue to increase investment in EMS design and strive to make the performance of FCHEV close to the high power, high efficiency and driving comfort of traditional diesel locomotive.

5.3. Directions and challenges of FCHEV In the commercialization process of FCHEV, there are many challenges and controversies. From a technology standpoint, alternatives to ICE cars will eventually emerge. Instead of rejecting the bad effects of new things, what we should do is to keep improving until they are fully accepted by the society. In this part, the problems existing in the future optimization process of FCHEV are analyzed in combination with the above review, and the author's views on the optimization direction are elaborated. According to the attention and efforts of scholars in the world, FCHEV is expected to become the main solution to the energy and environmental crisis in the automotive industry, rather than just a paper-based solution. 5.3.1. Traditional optimization problems with EMS The satisfaction of power performance is the first condition of automobile. At present, many researches only solve the problem of power distribution without considering whether the distribution scheme is suitable for components, which will affect the service life of components, such as FC, battery, etc. This negligence will lead to an increase in the total life cycle cost of the vehicle. Therefore, recently, some EMS put the performance constraints of these components into the optimization constraints. In the optimization problem, most researchers will consider the long-term factors of hydrogen or fuel consumption in the optimization process. The researchers found that the off-line strategy can’t adapt to the random variables in the optimization process well, while the real-time strategy can perform better. But the other problem is that, because the calculation of the algorithm is too large and difficult to realize, in many practical control devices, the most commonly used PID controller is still very popular. Therefore, it is necessary to develop advanced optimization algorithm to improve the weight of optimization objectives. Further improve the hydrogen reduction efficiency of FC, reduce energy consumption, reduce maintenance costs, and solve the problem of oxygen deficiency in the operation of the system, and use the actual experimental platform to verify and improve the practicability.

5.3.3. Research on key technology optimization of hydrogenation facilities Carry out optimization research on key technologies of hydrogenation facilities, and accelerate the formulation of design, construction, operation management specifications and relevant technical standards for hydrogenation facilities. Research and development of charging facilities, network connection, monitoring, metering, billing equipment and technology, research and application of vehicle network integration technology, and explore the mechanism of new energy vehicles as mobile energy storage units and power grid to achieve twoway interaction of energy and information. The framework agreement of fuel cell module assembly was signed to accelerate the industrialization process of FCHEV.

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Acknowledgement [25]

This project is supported by the National Natural Science Foundation of the China (Grant No. 51405286) and Shanghai Key Laboratory Power Station Automation Technology Laboratory (Grant No. 13DZ2273800). In addition, we are particularly grateful to all the researchers and experts working in the field of the fuel cell hybrid drive robots and system evaluation. Their excellent research results are very helpful to our research and promote the completion of this manuscript. Special thanks to the editors and reviewers for their serious and correct comments on this manuscript.

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